{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# 使用图片测试网络\r\n",
    "使用时需要修改 *cell[4]* 中的 *`filepath`* 和 *cell[5]、cell[6]* 中的图片路径"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "%matplotlib inline"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "import neuralnetwork\r\n",
    "import glob"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "# number of input, hidden and output nodes\r\n",
    "input_nodes = 784\r\n",
    "hidden_nodes = 400\r\n",
    "output_nodes = 10\r\n",
    "\r\n",
    "# learning rate\r\n",
    "learning_rate = 0.01"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "filepath = \"saved_data/Wed Sep  8 21_47_38 2021/\"\r\n",
    "\r\n",
    "wih, who = neuralnetwork.read_w_matrix(filepath)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "# 识别单张图片\r\n",
    "img = \"./my_nums/1.png\"\r\n",
    "\r\n",
    "n = neuralnetwork.trainedNetwork(input_nodes, hidden_nodes, output_nodes, learning_rate, wih, who)\r\n",
    "r = neuralnetwork.testsingle(n, neuralnetwork.img2data(img), True)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "loading ./my_nums/1.png ...\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "# 识别目录中所有以 *.png 命名的图片\r\n",
    "n = neuralnetwork.trainedNetwork(\r\n",
    "    input_nodes, hidden_nodes, output_nodes, learning_rate, wih, who)\r\n",
    "for image_file_name in glob.glob('./my_nums/*.png'):\r\n",
    "    r = neuralnetwork.testsingle(n, neuralnetwork.img2data(image_file_name), True)\r\n",
    "\r\n",
    "    # input(\"press enter to continue\")\r\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "loading ./my_nums\\0.png ...\n",
      "loading ./my_nums\\1.png ...\n",
      "loading ./my_nums\\2.png ...\n",
      "loading ./my_nums\\3.png ...\n",
      "loading ./my_nums\\4.png ...\n",
      "loading ./my_nums\\5.png ...\n",
      "loading ./my_nums\\6.png ...\n",
      "loading ./my_nums\\7.png ...\n",
      "loading ./my_nums\\8.png ...\n",
      "loading ./my_nums\\9.png ...\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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WW3LbWzm2Ys6cOU0/t1d5z26WCIfdLBEOu1kiHHazRDjsZolw2M0S4bCbJaLuOLukKcCtwIFUrs++PCKulrQEOA8YylZdHBEPdapQs+F8bMXoNHJQzQ7g4oh4TtK+wLOSHs3afhAR3+9ceWbWLnXDHhGbgE3Z/e2S1gIHdbowM2uvUX1mlzQNOBr4RbboQkmrJd0kaUKN5yyUVJZUHhoaGmkVM+uChsMuaR/gXuAbEfEOcD1wOHAUlT3/0pGeFxHLI6IUEaWBgYE2lGxmzWgo7JJ2pxL0OyLiPoCI2BwRH0XEx8ANwLGdK9PMWlU37JIE3AisjYirqpZPrlptDrCm/eWZWbs08m388cA5wIuSns+WLQbOknQUEMB64GsdqdDM2qKRb+OfBjRCk8fUzcYQH0FnlgiH3SwRDrtZIhx2s0Q47GaJcNjNEuGwmyXCYTdLhMNulgiH3SwRDrtZIhx2s0Q47GaJcNjNEqGI6F5n0hDwWtWiicDWrhUwOr1aW6/WBa6tWe2sbWpEjHj+t66G/ROdS+WIKBVWQI5era1X6wLX1qxu1ea38WaJcNjNElF02JcX3H+eXq2tV+sC19asrtRW6Gd2M+ueovfsZtYlDrtZIgoJu6RTJf1S0iuSLi2ihlokrZf0oqTnJZULruUmSVskralatr+kRyWty36OeI29gmpbImlj9to9L2lGQbVNkfSEpLWSXpJ0Uba80Ncup66uvG5d/8wuaRzwH8AXgA3AM8BZEfFyVwupQdJ6oBQRhR+AIelE4F3g1oj4bLbse8C2iLgy+0M5ISK+1SO1LQHeLfoy3tnViiZXX2YcOB2YT4GvXU5dZ9KF162IPfuxwCsR8WpEfAD8CJhdQB09LyKeArYNWzwbGMzuD1L5Zem6GrX1hIjYFBHPZfe3AzsvM17oa5dTV1cUEfaDgDeqHm+gt673HsAjkp6VtLDoYkYwKSI2QeWXBzig4HqGq3sZ724adpnxnnntmrn8eauKCPtIl5LqpfG/4yPiGOA0YFH2dtUa09BlvLtlhMuM94RmL3/eqiLCvgGYUvX4YODNAuoYUUS8mf3cAtxP712KevPOK+hmP7cUXM//6aXLeI90mXF64LUr8vLnRYT9GWC6pEMl7QF8GXiwgDo+QdLe2RcnSNobOIXeuxT1g8C87P484IECa9lFr1zGu9Zlxin4tSv88ucR0fUbMIPKN/K/Ar5dRA016joMeCG7vVR0bcCdVN7WfUjlHdFXgd8AVgHrsp/791BttwEvAqupBGtyQbWdQOWj4Wrg+ew2o+jXLqeurrxuPlzWLBE+gs4sEQ67WSIcdrNEOOxmiXDYzRLhsJslwmE3S8T/Ap6nCKefgrJTAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  }
 ],
 "metadata": {
  "orig_nbformat": 4,
  "language_info": {
   "name": "python",
   "version": "3.7.4",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.7.4 64-bit ('base': conda)"
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  "interpreter": {
   "hash": "a8f61be024eba58adef938c9aa1e29e02cb3dece83a5348b1a2dafd16a070453"
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}